docstr: Get the scalar from model train summary.
  
  This method will return a list of summary data of
  [iteration_number, scalar_value, timestamp].
  
  :param tag: The string variable represents the scalar wanted
docstr_tok: ["Get","the","scalar","from","model","train","summary",".","This","method","will","return","a","list","of","summary","data","of","[","iteration_number",",","scalar_value",",","timestamp","]",".",":","param","tag",":","The","string","variable","represents","the","scalar","wanted"]
code: def get_train_summary(self, tag=None):
      """
      Get the scalar from model train summary.
  
      This method will return a list of summary data of
      [iteration_number, scalar_value, timestamp].
  
      :param tag: The string variable represents the scalar wanted
      """
      # Exception handle
      if (
          tag != "Loss"
          and tag != "LearningRate"
          and tag != "Throughput"
      ):
          raise TypeError(
              'Only "Loss", "LearningRate", "Throughput"'
              + "are supported in train summary"
          )
      if self.is_nnframe_fit:
          train_summary = self.nn_estimator.getTrainSummary()
          return train_summary.read_scalar(tag=tag)
      else:
          return self.estimator.get_train_summary(tag=tag)
  
imports: from zoo.orca.learn.metrics import Metric
  from zoo.orca.learn.utils import bigdl_metric_results_to_dict
  from zoo.pipeline.nnframes import NNEstimator, NNModel
  from zoo.pipeline.estimator import Estimator
  from zoo.orca.learn.spark_estimator import Estimator
  from zoo.orca.data import SparkXShards
  from bigdl.optim.optimizer import MaxEpoch
  from zoo.feature.common import FeatureSet
  from zoo.orca.learn.metrics import Accuracy
  from pyspark.sql.dataframe import DataFrame
label_desc: Use image node_id 12 for calling the BigDLEstimator obj's underlying member method code with example usage: obj.get_train_summary(tag) and returns: train_summary, self